The purpose of this study is to investigate the mediator role of solidarity and intellectual capital elements within technocities on the relationship between resource dependency sub-dimensions and innovation performance. Research data’s obtained from 234 participants who work in technocities were tested with structural equation model. Resource dependency dimensions, which are independent variables, were evaluated in two sub-dimensions; the importance of the resource (RIMP) and the availability of alternatives (AA). Uncertainty scale was evaluated in three sub-dimensions; ability to be a resource (AbiR), resource scarcity (RS) and resource interconnectedness (RINT). Mediating variables were planned as solidarity (SOL) and intellectual capital- human capital (H_C), social capital (S_C) and organizational capital (O_C)- The results of the analysis emphasized the importance of solidarity and intellectual capital in terms of dependency and uncertainty. In analyzing the mediation effects, three different models were tested and were determined to be parallel. The SOL and O_C dependents found to have a direct effect on the innovation performance (INP). According to Model 1; it can be stated that the RIMP has a direct and positive effect on INP, O_C and SOL. In the model 2; AA has a direct and positive effect on INP, O_C and SOL. In Model 3, the RINT has a direct effect on the INP, O_C and SOL. Based on AIC and R2 values calculated for comparison the SOL and O_C, the second model found to be best model among the others.

Refugees are people fleeing conflict or persecution. because of their race, religion, nationality,membership in a particular social group, or political opinion. Refugees are defined and protected in international law, and must not be expelled or returned to situations where their life and freedom are at risk. The final refugees numbers used in this study were collected based on the given definition. In addition to obtained numbers, we also added socio-economic variables which affect ‘’ being a refugee’’.The data set used in this study includes both socio-economic attributes of refugees from 215 countries beetween 2008 and 2013 categorized refugee numbers. We used common data mining techniques such as Naive Bayes Decision Trees and K- Nearest Neighborhood classification algorithm in order to compare classification ratios. According to our study we obtained best classification ratio with K-Neighborhood algorithm with k-fold cross validation. In order to increase the classification success rate, it is also applied Principle Component Analysis. As a result of dimension reduction, better classification results were observed.